


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):
 _lowercase:       Tuple						=				old_name

 if "patch_embed" in old_name:
  _lowercase:       Optional[Any]						=				old_name.split("."      )

  if layer == "0":
   _lowercase:       List[str]						=				old_name.replace("0" ,   "convolution1"      )
  elif layer == "1":
   _lowercase:       Union[str, Any]						=				old_name.replace("1" ,   "batchnorm_before"      )
  elif layer == "3":
   _lowercase:       str						=				old_name.replace("3" ,   "convolution2"      )
  else:
   _lowercase:       Optional[int]						=				old_name.replace("4" ,   "batchnorm_after"      )

 if "network" in old_name and re.search(R"\d\.\d" ,   _A      ):
  _lowercase:       Union[str, Any]						=				r'\b\d{2}\b'
  if bool(re.search(_A ,   _A      )      ):
   _lowercase:       List[str]						=				re.search(R"\d\.\d\d." ,   _A      ).group()
  else:
   _lowercase:       int						=				re.search(R"\d\.\d." ,   _A      ).group()
  if int(match[0]      ) < 6:
   _lowercase:       Dict						=				old_name.replace(_A ,   ""      )
   _lowercase:       List[Any]						=				trimmed_name.replace("network" ,   match[0] + ".meta4D_layers.blocks." + match[2:-1]      )
   _lowercase:       Any						=				'intermediate_stages.' + trimmed_name
  else:
   _lowercase:       str						=				old_name.replace(_A ,   ""      )
   if int(match[2]      ) < num_meta4D_last_stage:
    _lowercase:       Union[str, Any]						=				trimmed_name.replace("network" ,   "meta4D_layers.blocks." + match[2]      )
   else:
    _lowercase:       str						=				str(int(match[2]      ) - num_meta4D_last_stage      )
    _lowercase:       List[Any]						=				trimmed_name.replace("network" ,   "meta3D_layers.blocks." + layer_index      )
    if "norm1" in old_name:
     _lowercase:       Dict						=				trimmed_name.replace("norm1" ,   "layernorm1"      )
    elif "norm2" in old_name:
     _lowercase:       Union[str, Any]						=				trimmed_name.replace("norm2" ,   "layernorm2"      )
    elif "fc1" in old_name:
     _lowercase:       Optional[int]						=				trimmed_name.replace("fc1" ,   "linear_in"      )
    elif "fc2" in old_name:
     _lowercase:       Union[str, Any]						=				trimmed_name.replace("fc2" ,   "linear_out"      )

   _lowercase:       Optional[int]						=				'last_stage.' + trimmed_name

 elif "network" in old_name and re.search(R".\d." ,   _A      ):
  _lowercase:       List[Any]						=				old_name.replace("network" ,   "intermediate_stages"      )

 if "fc" in new_name:
  _lowercase:       Any						=				new_name.replace("fc" ,   "convolution"      )
 elif ("norm1" in new_name) and ("layernorm1" not in new_name):
  _lowercase:       List[Any]						=				new_name.replace("norm1" ,   "batchnorm_before"      )
 elif ("norm2" in new_name) and ("layernorm2" not in new_name):
  _lowercase:       Optional[int]						=				new_name.replace("norm2" ,   "batchnorm_after"      )
 if "proj" in new_name:
  _lowercase:       Any						=				new_name.replace("proj" ,   "projection"      )
 if "dist_head" in new_name:
  _lowercase:       Dict						=				new_name.replace("dist_head" ,   "distillation_classifier"      )
 elif "head" in new_name:
  _lowercase:       List[Any]						=				new_name.replace("head" ,   "classifier"      )
 elif "patch_embed" in new_name:
  _lowercase:       str						=				'efficientformer.' + new_name
 elif new_name == "norm.weight" or new_name == "norm.bias":
  _lowercase:       List[Any]						=				new_name.replace("norm" ,   "layernorm"      )
  _lowercase:       Dict						=				'efficientformer.' + new_name
 else:
  _lowercase:       Optional[Any]						=				'efficientformer.encoder.' + new_name

 return new_name


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):
 for key in checkpoint.copy().keys():
  _lowercase:       Union[str, Any]						=				checkpoint.pop(_A      )
  _lowercase:       Optional[int]						=				val

 return checkpoint


def 							__lowerCAmelCase      (			):
 _lowercase:       List[str]						=				'http://images.cocodataset.org/val2017/000000039769.jpg'
 _lowercase:       Any						=				Image.open(requests.get(_A ,   stream=_A      ).raw      )

 return image


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 _lowercase:       Optional[Any]						=				torch.load(_A ,   map_location="cpu"      )['model']
 _lowercase:       List[Any]						=				EfficientFormerConfig.from_json_file(_A      )
 _lowercase:       Any						=				EfficientFormerForImageClassificationWithTeacher(_A      )
 _lowercase:       List[str]						=				'_'.join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

 _lowercase:       int						=				config.depths[-1] - config.num_metaad_blocks + 1
 _lowercase:       List[Any]						=				convert_torch_checkpoint(_A ,   _A      )

 model.load_state_dict(_A      )
 model.eval()

 _lowercase:       List[str]						=				{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 # prepare image
 _lowercase:       str						=				prepare_img()
 _lowercase:       Optional[Any]						=				2_5_6
 _lowercase:       Dict						=				2_2_4
 _lowercase:       Any						=				EfficientFormerImageProcessor(
     size={"shortest_edge": image_size} ,   crop_size={"height": crop_size, "width": crop_size} ,   resample=pillow_resamplings["bicubic"] ,   )
 _lowercase:       Union[str, Any]						=				processor(images=_A ,   return_tensors="pt"      ).pixel_values

 # original processing pipeline
 _lowercase:       Optional[int]						=				Compose(
     [
         Resize(_A ,   interpolation=pillow_resamplings["bicubic"]      ),
         CenterCrop(_A      ),
         ToTensor(),
         Normalize(_A ,   _A      ),
     ]      )
 _lowercase:       Optional[Any]						=				image_transforms(_A      ).unsqueeze(0      )

 assert torch.allclose(_A ,   _A      )

 _lowercase:       Tuple						=				model(_A      )
 _lowercase:       Tuple						=				outputs.logits

 _lowercase:       Any						=				(1, 1_0_0_0)

 if "l1" in model_name:
  _lowercase:       str						=				torch.Tensor(
      [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]      )
  assert torch.allclose(logits[0, :1_0] ,   _A ,   atol=1e-3      )
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  _lowercase:       Optional[Any]						=				torch.Tensor(
      [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]      )
  assert torch.allclose(logits[0, :1_0] ,   _A ,   atol=1e-3      )
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  _lowercase:       Any						=				torch.Tensor(
      [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]      )
  assert logits.shape == expected_shape
 else:
  raise ValueError(
      f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"      )

 # Save Checkpoints
 Path(_A      ).mkdir(exist_ok=_A      )
 model.save_pretrained(_A      )
 print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"      )
 processor.save_pretrained(_A      )
 print(f"Processor successfuly saved at {pytorch_dump_path}"      )

 if push_to_hub:
  print("Pushing model to the hub..."      )

  model.push_to_hub(
      repo_id=f"Bearnardd/{pytorch_dump_path}" ,   commit_message="Add model" ,   use_temp_dir=_A ,   )
  processor.push_to_hub(
      repo_id=f"Bearnardd/{pytorch_dump_path}" ,   commit_message="Add image processor" ,   use_temp_dir=_A ,   )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			int        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--pytorch_model_path',
           default=None,
           type=str,
           required=True,
           help='Path to EfficientFormer pytorch checkpoint.',
       )
       parser.add_argument(
           '--config_file',
           default=None,
           type=str,
           required=True,
           help='The json file for EfficientFormer model config.',
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )

       parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
       parser.add_argument(
           '--no-push_to_hub',
           dest='push_to_hub',
           action='store_false',
           help='Do not push model and image processor to the hub',
       )
       parser.set_defaults(push_to_hub=True)

       _SCREAMING_SNAKE_CASE     :			List[Any]        =							parser.parse_args()
       convert_efficientformer_checkpoint(
           checkpoint_path=args.pytorch_model_path,
           efficientformer_config_file=args.config_file,
           pytorch_dump_path=args.pytorch_dump_path,
           push_to_hub=args.push_to_hub,
       )






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Any					=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """pegasus"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Tuple=5_02_65		,     __snake_case					:    List[Any]=10_24		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    str=16		,     __snake_case					:    int=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    int=16		,     __snake_case					:    Tuple=0.0		,     __snake_case					:    Any=0.0		,     __snake_case					:    Any=True		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    Any="gelu"		,     __snake_case					:    str=10_24		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.0		,     __snake_case					:    str=0.0		,     __snake_case					:    str=0.02		,     __snake_case					:    Union[str, Any]=0		,     __snake_case					:    List[Any]=False		,     __snake_case					:    Optional[Any]=0		,     __snake_case					:    str=1		,     __snake_case					:    Any=1		,     **__snake_case					:    int		,     ):
      a		:   Optional[int]	  =	vocab_size
      a		:   List[str]	  =	max_position_embeddings
      a		:   List[Any]	  =	d_model
      a		:   Tuple	  =	encoder_ffn_dim
      a		:   List[str]	  =	encoder_layers
      a		:   str	  =	encoder_attention_heads
      a		:   str	  =	decoder_ffn_dim
      a		:   Optional[int]	  =	decoder_layers
      a		:   Optional[int]	  =	decoder_attention_heads
      a		:   Optional[int]	  =	dropout
      a		:   Any	  =	attention_dropout
      a		:   str	  =	activation_dropout
      a		:   List[Any]	  =	activation_function
      a		:   Union[str, Any]	  =	init_std
      a		:   List[Any]	  =	encoder_layerdrop
      a		:   Union[str, Any]	  =	decoder_layerdrop
      a		:   List[Any]	  =	use_cache
      a		:   Any	  =	encoder_layers
      a		:   Optional[int]	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__snake_case		,     eos_token_id=__snake_case		,     is_encoder_decoder=__snake_case		,     decoder_start_token_id=__snake_case		,     forced_eos_token_id=__snake_case		,     **__snake_case		,     )







    @property
    def      lowercase_			( self					:    int      ):
      return self.encoder_attention_heads







    @property
    def      lowercase_			( self					:    int      ):
      return self.d_model